DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

• Published in: Earth system science data 2020-09, Vol.12 (3), p.2209-2221
• Main Authors: Hao, Dalei, Asrar, Ghassem R., Zeng, Yelu, Zhu, Qing, Wen, Jianguang, Xiao, Qing, Chen, Min
• Format: Article
• Language: English
• Subjects: Accuracy; Atmospheric aerosols; Atmospheric models; Clouds; Daily; Data; Deep Space Climate Observatory; Diurnal; Earth; Energy; ENVIRONMENTAL SCIENCES; Federal agencies; Fluxes; Ground stations; Ground-based observation; Intercalibration; Learning algorithms; Machine learning; Mapping; Photosynthetically active radiation; Photovoltaics; Quality; Radiation; Radiation budget; Radiation data; Real time; Remote sensing; Remote sensing techniques; Resolution; Satellite tracking; Satellites; Seasonal variability; Seasonal variation; Seasonal variations; Sensing techniques; Sensors; Short wave radiation; Solar energy; Spatial discrimination; Spatial resolution; Synchronous satellites; System dynamics; Temporal resolution; Terrestrial environments
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-12-2209-2020

Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1∘×0.1∘), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of −3.96 and −0.71 W m−2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m−2, respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial s